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Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Social Simulation Study

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  • Whan-Seon Kim

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Abstract

This paper models a supply network as a complex adaptive system (CAS), in which firms or agents interact with one another and adapt themselves. And it applies agent-based social simulation (ABSS), a research method of simulating social systems under the CAS paradigm, to observe emergent outcomes. The main purposes of this paper are to consider a social factor, trust, in modeling the agents' behavioral decision-makings and, through the simulation studies, to examine the intermediate self-organizing processes and the resulting macro-level system behaviors. The simulations results reveal symmetrical trust levels between two trading agents, based on which the degree of trust relationship in each pair of trading agents as well as the resulting collaboration patterns in the entire supply network emerge. Also, it is shown that agents' decision-making behavior based on the trust relationship can contribute to the reduction in the variability of inventory levels. This result can be explained by the fact that mutual trust relationship based on the past experiences of trading diminishes an agent's uncertainties about the trustworthiness of its trading partners and thereby tends to stabilize its inventory levels.

Suggested Citation

  • Whan-Seon Kim, 2009. "Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Social Simulation Study," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(3), pages 1-4.
  • Handle: RePEc:jas:jasssj:2009-6-2
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    File URL: http://jasss.soc.surrey.ac.uk/12/3/4/4.pdf
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    References listed on IDEAS

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    1. Dmytro Tykhonov & Catholijn Jonker & Sebastiaan Meijer & Tim Verwaart, 2008. "Agent-Based Simulation of the Trust and Tracing Game for Supply Chains and Networks," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(3), pages 1-1.
    2. Troy J Strader & Fu-ren Lin & Michael J Shaw, 1998. "Simulation of Order Fulfillment in Divergent Assembly Supply Chains," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(2), pages 1-5.
    3. Paul Hart & Carol Saunders, 1997. "Power and Trust: Critical Factors in the Adoption and Use of Electronic Data Interchange," Organization Science, INFORMS, vol. 8(1), pages 23-42, February.
    4. Paul Davidsson, 2002. "Agent Based Social Simulation: a Computer Science View," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(1), pages 1-7.
    5. Erin Anderson & Barton Weitz, 1989. "Determinants of Continuity in Conventional Industrial Channel Dyads," Marketing Science, INFORMS, vol. 8(4), pages 310-323.
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    Cited by:

    1. Gao, Lin, 2017. "Between Trust and Performance: Exploring Socio-Economic Mechanisms on Directed Weighted Regular Ring with Agent-Based Modeling," MPRA Paper 78428, University Library of Munich, Germany.
    2. Shu-Heng Chen & Bin-Tzong Chie & Tong Zhang, 2015. "Network-Based Trust Games: An Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, pages 1-5.
    3. Paola Tubaro, 2011. "Computational Economics," Chapters,in: The Elgar Companion to Recent Economic Methodology, chapter 10 Edward Elgar Publishing.
    4. Gao, Lin, 2016. "Trust and Performance: Exploring Socio-Economic Mechanisms in the “Deep” Network Structure with Agent-Based Modeling," MPRA Paper 75214, University Library of Munich, Germany.

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